Knowledge Enhanced Multi-intent Transformer Network for Recommendation

Résumé fourni par la source

Incorporating Knowledge Graphs (KGs) into Recommendation has attracted growing attention in industry, due to the great potential of KG in providing abundant supplementary information and interpretability for the underlying models. However, simply integrating KG into recommendation usually brings in negative feedback in industry, mainly due to the ignorance of the following two factors: i) users' multiple intents, which involve diverse nodes in KG. For example, in e-commerce scenarios, users may exhibit preferences for specific styles, brands, or colors. ii) knowledge noise, which is a prevalent issue in Knowledge Enhanced Recommendation (KGR) and even more severe in industry scenarios. The irrelevant knowledge properties of items may result in inferior model performance compared to approaches that do not incorporate knowledge. To tackle these challenges, we propose a novel approach named Knowledge Enhanced Multi-intent Transformer Network for Recommendation (KGTN), which comprises two primary modules: Global Intents Modeling with Graph Transformer, and Knowledge Contrastive Denoising under Intents. Specifically, Global Intents with Graph Transformer focuses on capturing learnable user intents, by incorporating global signals from user-item-relation-entity interactions with a well-designed graph transformer, and meanwhile learning intent-aware user/item representations. On the other hand, Knowledge Contrastive Denoising under Intents is dedicated to learning precise and robust representations. It leverages the intent-aware user/item representations to sample relevant knowledge, and subsequently proposes a local-global contrastive mechanism to enhance noise-irrelevant representation learning. Extensive experiments conducted on three benchmark datasets show the superior performance of our proposed method over the state-of-the-arts. And online A/B testing results on Alibaba large-scale industrial recommendation platform also indicate the real-scenario effectiveness of KGTN. The implementations are available at: https://github.com/CCIIPLab/KGTN.

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